ML Anthology

Appearance Learning by Adaptive Kalman Filters for FLIR Tracking

Vijay Venkataraman, Guoliang Fan, Xin Fan, Joseph P. Havlicek
CVPRW 2009 pp. 46-53
doi:10.1109/CVPRW.2009.5205206 /cvprw/2009/venkataraman2009cvprw-appearance/

Abstract

This paper addresses the challenging issue of target tracking and appearance learning in Forward Looking Infrared (FLIR) sequences. Tracking and appearance learning are formulated as a joint state estimation problem with two parallel inference processes. Specifically, a new adaptive Kalman filter is proposed to learn histogram-based target appearances. A particle filter is used to estimate the target position and size, where the learned appearance plays an important role. Our appearance learning algorithm is compared against two existing methods and experiments on the AMCOM FLIR dataset validate its effectiveness.

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Cite

Text

Venkataraman et al. "Appearance Learning by Adaptive Kalman Filters for FLIR Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5205206

Markdown

[Venkataraman et al. "Appearance Learning by Adaptive Kalman Filters for FLIR Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/venkataraman2009cvprw-appearance/) doi:10.1109/CVPRW.2009.5205206

BibTeX

@inproceedings{venkataraman2009cvprw-appearance,
  title     = {{Appearance Learning by Adaptive Kalman Filters for FLIR Tracking}},
  author    = {Venkataraman, Vijay and Fan, Guoliang and Fan, Xin and Havlicek, Joseph P.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2009},
  pages     = {46-53},
  doi       = {10.1109/CVPRW.2009.5205206},
  url       = {https://mlanthology.org/cvprw/2009/venkataraman2009cvprw-appearance/}
}